3. Main questions
Question 1: Pedicted GI50-values
As we mentioned in our presentation, we want to create a model to predict GI50-values thus to predict, if Lapatinib is a good choice., The first linear model trys to predict the G-50 value under the data of the doubling time.
Fold_ChangeLap = select(Fold_Change, contains("Lapa"))
NegLogGI50Lap = NegLogGI50[9,]
means = colMeans(Fold_ChangeLap)
Fold_Changemeans = as.data.frame(t(means))
a2 = gsub(x = colnames (Fold_Changemeans), pattern = "_lapatinib_10000nM_24h", replacement = "")
colnames(Fold_Changemeans) = a2
a3 = gsub(x = a2, pattern = "X7", replacement = "7")
colnames(Fold_Changemeans) = a3
a1 = gsub(x = colnames (NegLogGI50Lap), pattern = "-", replacement = ".")
colnames(NegLogGI50Lap) = a1
c1 = rbind(a1,NegLogGI50Lap)
c2 = rbind(a3,Fold_Changemeans)
c1 = t(c1)
c2 = t(c2)
c1 =as.data.frame(c1)
c2 =as.data.frame(c2)
c3 = subset(c1, `1` %in% intersect(c1$`1`, c2$V1))
c4 = as.numeric(as.character(c3$lapatinib))
adjustedNeglogI50Lap = as.data.frame(c4)
Fold_Changemeans = as.data.frame(t(Fold_Changemeans))
combined1 = cbind(adjustedNeglogI50Lap, Fold_Changemeans)
names1 = c( "NegLogI50Lap","Fold_Changemeans")
colnames(combined1) = names1
lmFold = lm(NegLogI50Lap ~ Fold_Changemeans, data = combined1)
summary(lmFold)##
## Call:
## lm(formula = NegLogI50Lap ~ Fold_Changemeans, data = combined1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0574 -0.4099 -0.1873 0.2076 2.0682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.464e+00 9.539e-02 57.281 <2e-16 ***
## Fold_Changemeans 1.913e+15 7.519e+14 2.544 0.014 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6822 on 52 degrees of freedom
## Multiple R-squared: 0.1107, Adjusted R-squared: 0.09355
## F-statistic: 6.47 on 1 and 52 DF, p-value: 0.01398qqnorm(lmFold$residuals, main = "Test for normaldistribution of residuals")
qqline(lmFold$residuals)
plot(combined1$NegLogI50Lap, lmFold$fitted.values, pch = 20, col = "blue", xlab = "Real values",
ylab = "Predicted values", main = "Comparison: real and predicted values ~ linear regression (Fold_Changemeans)")
abline(0, 1, col = "red")
cor(combined1$NegLogI50Lap,combined1$Fold_Changemeans)## [1] 0.3326477#Split the data (Training - Testing)
n = nrow(combined1)
rmse1 = sqrt(1/n * sum(lmFold$residuals^2))
rmse1## [1] 0.6694461i1.train = sample(1:nrow(combined1), 44)
dat1.train = combined1[i1.train, ]
dat1.test = combined1[-i1.train, ]
l1.train = lm(NegLogI50Lap ~ Fold_Changemeans, data = dat1.train)
summary(l1.train)##
## Call:
## lm(formula = NegLogI50Lap ~ Fold_Changemeans, data = dat1.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0928 -0.4019 -0.2109 0.2030 2.0015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.485e+00 1.082e-01 50.704 <2e-16 ***
## Fold_Changemeans 2.164e+15 9.354e+14 2.313 0.0257 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.699 on 42 degrees of freedom
## Multiple R-squared: 0.113, Adjusted R-squared: 0.09187
## F-statistic: 5.35 on 1 and 42 DF, p-value: 0.02569n = nrow(dat1.train)
rmse1.train = sqrt(1/n * sum(l1.train$residuals^2))
rmse1.train## [1] 0.6828938pred1 = predict(l1.train, newdata = dat1.test)
n = nrow(dat1.test)
residuals = dat1.test$NegLogI50Lap - pred1
rmse1.test1 = sqrt(1/n * sum(residuals^2))
rmse1.test1## [1] 0.6145976The second linear model trys to predict the G-50 value under the data of the Foldchange-means.
NegLogGI50Lap = NegLogGI50[9,]
#Sort by Cellline-Name
df = arrange(Cellline_Annotation, Cell_Line_Name)
Doublingtime = cbind.data.frame (df$Cell_Line_Name, df$Doubling_Time)
c21 = as.data.frame(t(NegLogGI50Lap))
combined2 = cbind(c21, Doublingtime$`df$Doubling_Time`)
names2 = c( "NegLogI50Lap","Doubling_Time")
colnames(combined2) = names2
combined2 =na.omit(combined2)
lmDouble = lm(NegLogI50Lap ~ Doubling_Time, data = combined2)
summary(lmDouble)##
## Call:
## lm(formula = NegLogI50Lap ~ Doubling_Time, data = combined2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2751 -0.4124 -0.1210 0.1709 2.0784
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.147415 0.245361 20.979 <2e-16 ***
## Doubling_Time 0.010536 0.006391 1.649 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6741 on 58 degrees of freedom
## Multiple R-squared: 0.04476, Adjusted R-squared: 0.0283
## F-statistic: 2.718 on 1 and 58 DF, p-value: 0.1046qqnorm(lmDouble$residuals, main = "Test for normaldistribution of residuals")
qqline(lmDouble$residuals)
plot(combined2$NegLogI50Lap, lmDouble$fitted.values, pch = 20, col = "blue", xlab = "Real values",
ylab = "Predicted values", main = "Comparison: real and predicted values ~ linear regression (Doubling-Time)")
abline(0, 1, col = "red")
cor(combined2$NegLogI50Lap,combined2$Doubling_Time)## [1] 0.2115772#Split the data (Training - Testing)
n = nrow(combined2)
rmse2 = sqrt(1/n * sum(lmDouble$residuals^2))
rmse2## [1] 0.6627233i2.train = sample(1:nrow(combined2), 48)
dat2.train = combined2[i2.train, ]
dat2.test = combined2[-i2.train, ]
l2.train = lm(NegLogI50Lap ~ Doubling_Time, data = dat2.train)
summary(l2.train)##
## Call:
## lm(formula = NegLogI50Lap ~ Doubling_Time, data = dat2.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9740 -0.3577 -0.1473 0.1776 1.9844
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.010389 0.251749 19.902 <2e-16 ***
## Doubling_Time 0.014262 0.006528 2.185 0.034 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6345 on 46 degrees of freedom
## Multiple R-squared: 0.09402, Adjusted R-squared: 0.07432
## F-statistic: 4.774 on 1 and 46 DF, p-value: 0.03403n = nrow(dat2.train)
rmse2.train = sqrt(1/n * sum(l2.train$residuals^2))
rmse2.train## [1] 0.6211052pred2 = predict(l2.train, newdata = dat2.test)
n = nrow(dat1.test)
residuals = dat2.test$NegLogI50Lap - pred2
rmse2.test = sqrt(1/n * sum(residuals^2))
rmse2.test## [1] 0.8938786As a last part, we did a multiple regression with both datasets to predict GI50-values.
b1 = gsub(x =Doublingtime$`df$Cell_Line_Name`, pattern = "-", replacement = ".")
Doublingtime1 = rbind(b1,Doublingtime$`df$Doubling_Time`)
Doublingtime1 = as.data.frame(t(Doublingtime1))
c31 = subset(Doublingtime1, b1 %in% intersect(Doublingtime1$b1, c2$V1))
c41 = as.numeric(as.character(c31$V2))
adjustedDoubling_Time = as.data.frame(c41)
combined3 = cbind(adjustedNeglogI50Lap, Fold_Changemeans, adjustedDoubling_Time)
names3 = c( "NegLogI50Lap","Fold_Changemeans","Doubling_Time")
colnames(combined3) = names3
mlr = lm(NegLogI50Lap ~ ., data = combined3)
summary(mlr)##
## Call:
## lm(formula = NegLogI50Lap ~ ., data = combined3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3123 -0.3909 -0.1226 0.2003 1.8141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.064e+00 2.655e-01 19.069 <2e-16 ***
## Fold_Changemeans 1.819e+15 7.429e+14 2.449 0.0178 *
## Doubling_Time 1.083e-02 6.717e-03 1.612 0.1130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6719 on 51 degrees of freedom
## Multiple R-squared: 0.1538, Adjusted R-squared: 0.1206
## F-statistic: 4.635 on 2 and 51 DF, p-value: 0.01415qqnorm(mlr$residuals, main = "Test for normaldistribution of residuals")
qqline(mlr$residuals)
plot(combined3$NegLogI50Lap, mlr$fitted.values, pch = 20, col = "blue", xlab = "Real values",
ylab = "Predicted values" , main = "Comparison: real and predicted values ~ multiple regression")
abline(0, 1, col = "red")
#Split the data (Training - Testing)
n = nrow(combined3)
rmse3 = sqrt(1/n * sum(mlr$residuals^2))
rmse3## [1] 0.6530072i3.train = sample(1:nrow(combined2), 44)
dat3.train = combined3[i3.train, ]
dat3.test = combined3[-i3.train, ]
l3.train = lm(NegLogI50Lap ~ ., data = dat3.train)
summary(l3.train)##
## Call:
## lm(formula = NegLogI50Lap ~ ., data = dat3.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4110 -0.4173 -0.1550 0.1519 1.7242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.001e+00 3.341e-01 14.972 <2e-16 ***
## Fold_Changemeans 1.727e+15 1.003e+15 1.722 0.0935 .
## Doubling_Time 1.355e-02 8.286e-03 1.636 0.1104
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.734 on 37 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.1461, Adjusted R-squared: 0.09993
## F-statistic: 3.165 on 2 and 37 DF, p-value: 0.05384n = nrow(dat3.train)
rmse3.train = sqrt(1/n * sum(l3.train$residuals^2))
rmse3.train## [1] 0.6730553pred3 = predict(l3.train, newdata = dat3.test)
n = nrow(dat3.test)
residuals = dat3.test$NegLogI50Lap - pred3
rmse3.test = sqrt(1/n * sum(residuals^2))
rmse3.test## [1] 0.4813735As you can see from the data, All three regression models are not really good. ##Question 2: Erlotinib vs Lapatinib
# correlation in general
n= as.data.frame(t(NegLogGI50))
rmv.rows = apply(n, 1, function(x) {
sum(is.na(x))
})
NLGI50.all = n[-which(rmv.rows > 0), ] # Removing any row with 1 or more missing values
rm(rmv.rows, n, NegLogGI50)
cor.mat = as.data.frame(cor(NLGI50.all[, 1:ncol(NLGI50.all)], method = "pearson")) #Pearson correlation
round(cor.mat, 2) #round values## 5-Azacytidine bortezomib cisplatin dasatinib doxorubicin
## 5-Azacytidine 1.00 -0.08 0.16 0.18 0.29
## bortezomib -0.08 1.00 0.01 -0.10 0.32
## cisplatin 0.16 0.01 1.00 -0.24 0.52
## dasatinib 0.18 -0.10 -0.24 1.00 -0.08
## doxorubicin 0.29 0.32 0.52 -0.08 1.00
## erlotinib 0.27 -0.32 0.01 0.42 -0.17
## geldanamycin 0.23 0.36 0.19 -0.09 0.23
## gemcitibine 0.16 -0.08 0.53 -0.03 0.37
## lapatinib 0.14 -0.26 -0.07 0.19 -0.16
## paclitaxel 0.10 0.20 0.01 -0.10 0.55
## sirolimus -0.05 0.01 0.27 0.07 0.17
## sorafenib 0.09 0.27 -0.01 -0.24 0.14
## sunitinib 0.12 -0.01 -0.05 -0.03 -0.14
## topotecan 0.14 0.13 0.55 0.02 0.60
## vorinostat 0.16 -0.02 0.07 -0.16 -0.06
## erlotinib geldanamycin gemcitibine lapatinib paclitaxel
## 5-Azacytidine 0.27 0.23 0.16 0.14 0.10
## bortezomib -0.32 0.36 -0.08 -0.26 0.20
## cisplatin 0.01 0.19 0.53 -0.07 0.01
## dasatinib 0.42 -0.09 -0.03 0.19 -0.10
## doxorubicin -0.17 0.23 0.37 -0.16 0.55
## erlotinib 1.00 -0.01 0.01 0.65 -0.37
## geldanamycin -0.01 1.00 0.12 -0.01 0.28
## gemcitibine 0.01 0.12 1.00 -0.15 0.03
## lapatinib 0.65 -0.01 -0.15 1.00 -0.24
## paclitaxel -0.37 0.28 0.03 -0.24 1.00
## sirolimus 0.21 -0.21 0.05 0.21 -0.04
## sorafenib -0.29 0.14 -0.01 -0.25 0.29
## sunitinib 0.06 0.24 0.06 0.12 -0.02
## topotecan -0.02 0.21 0.63 -0.14 0.20
## vorinostat 0.12 0.20 0.18 0.26 0.09
## sirolimus sorafenib sunitinib topotecan vorinostat
## 5-Azacytidine -0.05 0.09 0.12 0.14 0.16
## bortezomib 0.01 0.27 -0.01 0.13 -0.02
## cisplatin 0.27 -0.01 -0.05 0.55 0.07
## dasatinib 0.07 -0.24 -0.03 0.02 -0.16
## doxorubicin 0.17 0.14 -0.14 0.60 -0.06
## erlotinib 0.21 -0.29 0.06 -0.02 0.12
## geldanamycin -0.21 0.14 0.24 0.21 0.20
## gemcitibine 0.05 -0.01 0.06 0.63 0.18
## lapatinib 0.21 -0.25 0.12 -0.14 0.26
## paclitaxel -0.04 0.29 -0.02 0.20 0.09
## sirolimus 1.00 -0.11 -0.20 0.03 0.02
## sorafenib -0.11 1.00 0.05 0.16 0.10
## sunitinib -0.20 0.05 1.00 -0.05 -0.13
## topotecan 0.03 0.16 -0.05 1.00 0.08
## vorinostat 0.02 0.10 -0.13 0.08 1.00pairs(NLGI50.all[, 1:ncol(NLGI50.all)], pch = 20, cex = 0.8, col = "royalblue3", main = "Correlation_NegLogGI50") 
plot erlotinib all genes, coloured by tissue
#differece
diff = data.frame(erlotinib = NLGI50.all$erlotinib - mean(NLGI50.all$erlotinib), lapatinib = NLGI50.all$lapatinib- mean(NLGI50.all$lapatinib))
diff$celllines = rownames(NLGI50.all)
#create vector to insert column tissue from Metadata
tissue = sapply(1:nrow(diff), function(x) {
position = which(as.character(Metadata$cell) == diff[x, "celllines"])[1] #if tissue occurs several times, take the first
out = as.character(Metadata[position, "tissue"]) #output the tissue at this position
return(out)
})
diff$tissue = tissue
rm(tissue)
diff$celllines = factor(diff$celllines, levels = diff$celllines[order(diff$tissue)]) #Classified by tissue
ggplot(diff, aes(x = celllines, y = erlotinib, fill = tissue))+geom_bar(stat = "identity") + coord_flip() + labs(title = "Mean graph plot of NLGI50 values for Erlotinib")
The difference from the NegLogGI50 for a particular cell line and the mean NegLogGI50 is plotted here for Erlotinib.
plot lapatinib all genes, coloured by tissue
ggplot(diff, aes(x = celllines, y = lapatinib, fill = tissue)) + geom_bar(stat="identity") + coord_flip() + labs(title="Mean graph plot of NLGI50 values for Lapatinib")
The difference from the NegLogGI50 for a particular cell line and the mean NegLogGI50 is plotted here for Lapatinib.
correlation erlotinib , lapatinib
cor(NLGI50.all$erlotinib, NLGI50.all$lapatinib, method = "pearson")## [1] 0.6528188A Pearson correlation coefficient of ~ 0.65 confirms that these patterns are very similar
Lung genes
#only lung with mean all
### load data
Metadata_Lapatinib_treated = Metadata[which(Metadata$drug == "lapatinib" & Metadata$dose != "0nM"),]
NegLogGI50 = as.data.frame(readRDS(paste0(wd, "/Data/NegLogGI50.rds")))
#lung genes from Metadata
Lung_Metadata_L_treated = Metadata[which(Metadata$drug == "lapatinib" & Metadata$dose != "0nM" & Metadata$tissue == "Lung"),]
celllines = Lung_Metadata_L_treated$cell
NegLogGI50.lung = as.data.frame(t(NegLogGI50[c("erlotinib", "lapatinib"), celllines]))
#Difference
dif.NegLogGI50.lung = data.frame(erlotinib = NegLogGI50.lung$erlotinib - mean(NLGI50.all$erlotinib), lapatinib = NegLogGI50.lung$lapatinib - mean(NLGI50.all$lapatinib)) #erlotinib data - mean value, lapatinib data - mean value
dif.NegLogGI50.lung$celllines = rownames(NegLogGI50.lung)
# PLot
ggplot(dif.NegLogGI50.lung,aes(x = celllines, y = erlotinib)) + geom_bar(stat = "identity", fill = "skyblue") + geom_text(aes(label = round(erlotinib, 2)), vjust = -0.5, color = "black", size = 3) + coord_flip() + labs(title = "Mean graph plot of NLGI50 values for Erlotinib, only Lung genes")
plot lapatinib
ggplot(dif.NegLogGI50.lung,aes(x = celllines, y = lapatinib)) + geom_bar(stat = "identity", fill = "skyblue") + geom_text(aes(label=round(lapatinib, 2)), vjust = -0.5, color = "black", size = 3) + coord_flip() + labs(title = "Mean graph plot of NLGI50 values for Lapatinib, only Lung genes")
correlation lung
cor(NegLogGI50.lung$erlotinib, NegLogGI50.lung$lapatinib, method = "pearson")## [1] 0.9609488A pearson correlation coefficent of ~ 0.96 suggests that Lapatinib has a similar effect on lung cancer as Erlotinib
anova
<<<<<<< HEAD
selection of Lapatinib and Erlotinib treated cells
lapa<-data.frame(Metadata[which(Metadata[,'drug'] == "lapatinib"), ])
erlo<-data.frame(Metadata[which(Metadata[,'drug'] == "erlotinib"), ])
el<-right_join(lapa,erlo, by="cell")
el## sample.x cell drug.x dose.x time.x
## 1 786-0_lapatinib_10000nM_24h 786-0 lapatinib 10000nM 24h
## 2 786-0_lapatinib_0nM_24h 786-0 lapatinib 0nM 24h
## 3 A498_lapatinib_10000nM_24h A498 lapatinib 10000nM 24h
## 4 A498_lapatinib_0nM_24h A498 lapatinib 0nM 24h
## 5 A549_lapatinib_10000nM_24h A549 lapatinib 10000nM 24h
## 6 A549_lapatinib_0nM_24h A549 lapatinib 0nM 24h
## 7 ACHN_lapatinib_10000nM_24h ACHN lapatinib 10000nM 24h
## 8 ACHN_lapatinib_0nM_24h ACHN lapatinib 0nM 24h
## 9 BT-549_lapatinib_10000nM_24h BT-549 lapatinib 10000nM 24h
## 10 BT-549_lapatinib_0nM_24h BT-549 lapatinib 0nM 24h
## 11 CAKI-1_lapatinib_10000nM_24h CAKI-1 lapatinib 10000nM 24h
## 12 CAKI-1_lapatinib_0nM_24h CAKI-1 lapatinib 0nM 24h
## 13 <NA> CCRF-CEM <NA> <NA> <NA>
## 14 DU-145_lapatinib_10000nM_24h DU-145 lapatinib 10000nM 24h
## 15 DU-145_lapatinib_0nM_24h DU-145 lapatinib 0nM 24h
## 16 EKVX_lapatinib_10000nM_24h EKVX lapatinib 10000nM 24h
## 17 EKVX_lapatinib_0nM_24h EKVX lapatinib 0nM 24h
## 18 HCC-2998_lapatinib_10000nM_24h HCC-2998 lapatinib 10000nM 24h
## 19 HCC-2998_lapatinib_0nM_24h HCC-2998 lapatinib 0nM 24h
## 20 HCT-116_lapatinib_10000nM_24h HCT-116 lapatinib 10000nM 24h
## 21 HCT-116_lapatinib_0nM_24h HCT-116 lapatinib 0nM 24h
## 22 HCT-15_lapatinib_10000nM_24h HCT-15 lapatinib 10000nM 24h
## 23 HCT-15_lapatinib_0nM_24h HCT-15 lapatinib 0nM 24h
## 24 <NA> HL-60 <NA> <NA> <NA>
## 25 HOP-62_lapatinib_10000nM_24h HOP-62 lapatinib 10000nM 24h
## 26 HOP-62_lapatinib_0nM_24h HOP-62 lapatinib 0nM 24h
## 27 HOP-92_lapatinib_10000nM_24h HOP-92 lapatinib 10000nM 24h
## 28 HOP-92_lapatinib_0nM_24h HOP-92 lapatinib 0nM 24h
## 29 HS-578T_lapatinib_10000nM_24h HS-578T lapatinib 10000nM 24h
## 30 HS-578T_lapatinib_0nM_24h HS-578T lapatinib 0nM 24h
## 31 <NA> HT29 <NA> <NA> <NA>
## 32 IGR-OV1_lapatinib_10000nM_24h IGR-OV1 lapatinib 10000nM 24h
## 33 IGR-OV1_lapatinib_0nM_24h IGR-OV1 lapatinib 0nM 24h
## 34 <NA> K-562 <NA> <NA> <NA>
## 35 KM12_lapatinib_10000nM_24h KM12 lapatinib 10000nM 24h
## 36 KM12_lapatinib_0nM_24h KM12 lapatinib 0nM 24h
## 37 <NA> LOX <NA> <NA> <NA>
## 38 M14_lapatinib_10000nM_24h M14 lapatinib 10000nM 24h
## 39 M14_lapatinib_0nM_24h M14 lapatinib 0nM 24h
## 40 MALME-3M_lapatinib_10000nM_24h MALME-3M lapatinib 10000nM 24h
## 41 MALME-3M_lapatinib_0nM_24h MALME-3M lapatinib 0nM 24h
## 42 MCF7_lapatinib_10000nM_24h MCF7 lapatinib 10000nM 24h
## 43 MCF7_lapatinib_0nM_24h MCF7 lapatinib 0nM 24h
## 44 MDA-MB-231_lapatinib_10000nM_24h MDA-MB-231 lapatinib 10000nM 24h
## 45 MDA-MB-231_lapatinib_0nM_24h MDA-MB-231 lapatinib 0nM 24h
## 46 MDA-MB-435_lapatinib_10000nM_24h MDA-MB-435 lapatinib 10000nM 24h
## 47 MDA-MB-435_lapatinib_0nM_24h MDA-MB-435 lapatinib 0nM 24h
## 48 MDA-MB-468_lapatinib_10000nM_24h MDA-MB-468 lapatinib 10000nM 24h
## 49 MDA-MB-468_lapatinib_0nM_24h MDA-MB-468 lapatinib 0nM 24h
## 50 MOLT-4_lapatinib_10000nM_24h MOLT-4 lapatinib 10000nM 24h
## 51 MOLT-4_lapatinib_0nM_24h MOLT-4 lapatinib 0nM 24h
## 52 NCI-ADR-RES_lapatinib_10000nM_24h NCI-ADR-RES lapatinib 10000nM 24h
## 53 NCI-ADR-RES_lapatinib_0nM_24h NCI-ADR-RES lapatinib 0nM 24h
## 54 NCI-H226_lapatinib_10000nM_24h NCI-H226 lapatinib 10000nM 24h
## 55 NCI-H226_lapatinib_0nM_24h NCI-H226 lapatinib 0nM 24h
## 56 NCI-H23_lapatinib_10000nM_24h NCI-H23 lapatinib 10000nM 24h
## 57 NCI-H23_lapatinib_0nM_24h NCI-H23 lapatinib 0nM 24h
## 58 NCI-H322M_lapatinib_10000nM_24h NCI-H322M lapatinib 10000nM 24h
## 59 NCI-H322M_lapatinib_0nM_24h NCI-H322M lapatinib 0nM 24h
## 60 NCI-H460_lapatinib_10000nM_24h NCI-H460 lapatinib 10000nM 24h
## 61 NCI-H460_lapatinib_0nM_24h NCI-H460 lapatinib 0nM 24h
## 62 NCI-H522_lapatinib_10000nM_24h NCI-H522 lapatinib 10000nM 24h
## 63 NCI-H522_lapatinib_0nM_24h NCI-H522 lapatinib 0nM 24h
## 64 OVCAR-3_lapatinib_10000nM_24h OVCAR-3 lapatinib 10000nM 24h
## 65 OVCAR-3_lapatinib_0nM_24h OVCAR-3 lapatinib 0nM 24h
## 66 OVCAR-4_lapatinib_10000nM_24h OVCAR-4 lapatinib 10000nM 24h
## 67 OVCAR-4_lapatinib_0nM_24h OVCAR-4 lapatinib 0nM 24h
## 68 OVCAR-5_lapatinib_10000nM_24h OVCAR-5 lapatinib 10000nM 24h
## 69 OVCAR-5_lapatinib_0nM_24h OVCAR-5 lapatinib 0nM 24h
## 70 OVCAR-8_lapatinib_10000nM_24h OVCAR-8 lapatinib 10000nM 24h
## 71 OVCAR-8_lapatinib_0nM_24h OVCAR-8 lapatinib 0nM 24h
## 72 PC-3_lapatinib_10000nM_24h PC-3 lapatinib 10000nM 24h
## 73 PC-3_lapatinib_0nM_24h PC-3 lapatinib 0nM 24h
## 74 RPMI-8226_lapatinib_10000nM_24h RPMI-8226 lapatinib 10000nM 24h
## 75 RPMI-8226_lapatinib_0nM_24h RPMI-8226 lapatinib 0nM 24h
## 76 RXF-393_lapatinib_10000nM_24h RXF-393 lapatinib 10000nM 24h
## 77 RXF-393_lapatinib_0nM_24h RXF-393 lapatinib 0nM 24h
## 78 SF-268_lapatinib_10000nM_24h SF-268 lapatinib 10000nM 24h
## 79 SF-268_lapatinib_0nM_24h SF-268 lapatinib 0nM 24h
## 80 SF-295_lapatinib_10000nM_24h SF-295 lapatinib 10000nM 24h
## 81 SF-295_lapatinib_0nM_24h SF-295 lapatinib 0nM 24h
## 82 SF-539_lapatinib_10000nM_24h SF-539 lapatinib 10000nM 24h
## 83 SF-539_lapatinib_0nM_24h SF-539 lapatinib 0nM 24h
## 84 SK-MEL-2_lapatinib_10000nM_24h SK-MEL-2 lapatinib 10000nM 24h
## 85 SK-MEL-2_lapatinib_0nM_24h SK-MEL-2 lapatinib 0nM 24h
## 86 SK-MEL-28_lapatinib_10000nM_24h SK-MEL-28 lapatinib 10000nM 24h
## 87 SK-MEL-28_lapatinib_0nM_24h SK-MEL-28 lapatinib 0nM 24h
## 88 SK-MEL-5_lapatinib_10000nM_24h SK-MEL-5 lapatinib 10000nM 24h
## 89 SK-MEL-5_lapatinib_0nM_24h SK-MEL-5 lapatinib 0nM 24h
## 90 SK-OV-3_lapatinib_10000nM_24h SK-OV-3 lapatinib 10000nM 24h
## 91 SK-OV-3_lapatinib_0nM_24h SK-OV-3 lapatinib 0nM 24h
## 92 SN12C_lapatinib_10000nM_24h SN12C lapatinib 10000nM 24h
## 93 SN12C_lapatinib_0nM_24h SN12C lapatinib 0nM 24h
## 94 SNB-19_lapatinib_10000nM_24h SNB-19 lapatinib 10000nM 24h
## 95 SNB-19_lapatinib_0nM_24h SNB-19 lapatinib 0nM 24h
## 96 SNB-75_lapatinib_10000nM_24h SNB-75 lapatinib 10000nM 24h
## 97 SNB-75_lapatinib_0nM_24h SNB-75 lapatinib 0nM 24h
## 98 <NA> SR <NA> <NA> <NA>
## 99 SW-620_lapatinib_10000nM_24h SW-620 lapatinib 10000nM 24h
## 100 SW-620_lapatinib_0nM_24h SW-620 lapatinib 0nM 24h
## 101 T-47D_lapatinib_10000nM_24h T-47D lapatinib 10000nM 24h
## 102 T-47D_lapatinib_0nM_24h T-47D lapatinib 0nM 24h
## 103 TK-10_lapatinib_10000nM_24h TK-10 lapatinib 10000nM 24h
## 104 TK-10_lapatinib_0nM_24h TK-10 lapatinib 0nM 24h
## 105 U251_lapatinib_10000nM_24h U251 lapatinib 10000nM 24h
## 106 U251_lapatinib_0nM_24h U251 lapatinib 0nM 24h
## 107 UACC-257_lapatinib_10000nM_24h UACC-257 lapatinib 10000nM 24h
## 108 UACC-257_lapatinib_0nM_24h UACC-257 lapatinib 0nM 24h
## 109 UACC-62_lapatinib_10000nM_24h UACC-62 lapatinib 10000nM 24h
## 110 UACC-62_lapatinib_0nM_24h UACC-62 lapatinib 0nM 24h
## 111 UO-31_lapatinib_10000nM_24h UO-31 lapatinib 10000nM 24h
## 112 UO-31_lapatinib_0nM_24h UO-31 lapatinib 0nM 24h
## 113 786-0_lapatinib_10000nM_24h 786-0 lapatinib 10000nM 24h
## 114 786-0_lapatinib_0nM_24h 786-0 lapatinib 0nM 24h
## 115 A498_lapatinib_10000nM_24h A498 lapatinib 10000nM 24h
## 116 A498_lapatinib_0nM_24h A498 lapatinib 0nM 24h
## 117 A549_lapatinib_10000nM_24h A549 lapatinib 10000nM 24h
## 118 A549_lapatinib_0nM_24h A549 lapatinib 0nM 24h
## 119 ACHN_lapatinib_10000nM_24h ACHN lapatinib 10000nM 24h
## 120 ACHN_lapatinib_0nM_24h ACHN lapatinib 0nM 24h
## 121 BT-549_lapatinib_10000nM_24h BT-549 lapatinib 10000nM 24h
## 122 BT-549_lapatinib_0nM_24h BT-549 lapatinib 0nM 24h
## 123 CAKI-1_lapatinib_10000nM_24h CAKI-1 lapatinib 10000nM 24h
## 124 CAKI-1_lapatinib_0nM_24h CAKI-1 lapatinib 0nM 24h
## 125 <NA> CCRF-CEM <NA> <NA> <NA>
## 126 DU-145_lapatinib_10000nM_24h DU-145 lapatinib 10000nM 24h
## 127 DU-145_lapatinib_0nM_24h DU-145 lapatinib 0nM 24h
## 128 EKVX_lapatinib_10000nM_24h EKVX lapatinib 10000nM 24h
## 129 EKVX_lapatinib_0nM_24h EKVX lapatinib 0nM 24h
## 130 HCC-2998_lapatinib_10000nM_24h HCC-2998 lapatinib 10000nM 24h
## 131 HCC-2998_lapatinib_0nM_24h HCC-2998 lapatinib 0nM 24h
## 132 HCT-116_lapatinib_10000nM_24h HCT-116 lapatinib 10000nM 24h
## 133 HCT-116_lapatinib_0nM_24h HCT-116 lapatinib 0nM 24h
## 134 HCT-15_lapatinib_10000nM_24h HCT-15 lapatinib 10000nM 24h
## 135 HCT-15_lapatinib_0nM_24h HCT-15 lapatinib 0nM 24h
## 136 <NA> HL-60 <NA> <NA> <NA>
## 137 HOP-62_lapatinib_10000nM_24h HOP-62 lapatinib 10000nM 24h
## 138 HOP-62_lapatinib_0nM_24h HOP-62 lapatinib 0nM 24h
## 139 HOP-92_lapatinib_10000nM_24h HOP-92 lapatinib 10000nM 24h
## 140 HOP-92_lapatinib_0nM_24h HOP-92 lapatinib 0nM 24h
## 141 HS-578T_lapatinib_10000nM_24h HS-578T lapatinib 10000nM 24h
## 142 HS-578T_lapatinib_0nM_24h HS-578T lapatinib 0nM 24h
## 143 <NA> HT29 <NA> <NA> <NA>
## 144 IGR-OV1_lapatinib_10000nM_24h IGR-OV1 lapatinib 10000nM 24h
## 145 IGR-OV1_lapatinib_0nM_24h IGR-OV1 lapatinib 0nM 24h
## 146 <NA> K-562 <NA> <NA> <NA>
## 147 KM12_lapatinib_10000nM_24h KM12 lapatinib 10000nM 24h
## 148 KM12_lapatinib_0nM_24h KM12 lapatinib 0nM 24h
## 149 <NA> LOX <NA> <NA> <NA>
## 150 M14_lapatinib_10000nM_24h M14 lapatinib 10000nM 24h
## 151 M14_lapatinib_0nM_24h M14 lapatinib 0nM 24h
## 152 MALME-3M_lapatinib_10000nM_24h MALME-3M lapatinib 10000nM 24h
## 153 MALME-3M_lapatinib_0nM_24h MALME-3M lapatinib 0nM 24h
## 154 MCF7_lapatinib_10000nM_24h MCF7 lapatinib 10000nM 24h
## 155 MCF7_lapatinib_0nM_24h MCF7 lapatinib 0nM 24h
## 156 MDA-MB-231_lapatinib_10000nM_24h MDA-MB-231 lapatinib 10000nM 24h
## 157 MDA-MB-231_lapatinib_0nM_24h MDA-MB-231 lapatinib 0nM 24h
## 158 MDA-MB-435_lapatinib_10000nM_24h MDA-MB-435 lapatinib 10000nM 24h
## 159 MDA-MB-435_lapatinib_0nM_24h MDA-MB-435 lapatinib 0nM 24h
## 160 MDA-MB-468_lapatinib_10000nM_24h MDA-MB-468 lapatinib 10000nM 24h
## 161 MDA-MB-468_lapatinib_0nM_24h MDA-MB-468 lapatinib 0nM 24h
## 162 MOLT-4_lapatinib_10000nM_24h MOLT-4 lapatinib 10000nM 24h
## 163 MOLT-4_lapatinib_0nM_24h MOLT-4 lapatinib 0nM 24h
## 164 NCI-ADR-RES_lapatinib_10000nM_24h NCI-ADR-RES lapatinib 10000nM 24h
## 165 NCI-ADR-RES_lapatinib_0nM_24h NCI-ADR-RES lapatinib 0nM 24h
## 166 NCI-H226_lapatinib_10000nM_24h NCI-H226 lapatinib 10000nM 24h
## 167 NCI-H226_lapatinib_0nM_24h NCI-H226 lapatinib 0nM 24h
## 168 NCI-H23_lapatinib_10000nM_24h NCI-H23 lapatinib 10000nM 24h
## 169 NCI-H23_lapatinib_0nM_24h NCI-H23 lapatinib 0nM 24h
## 170 NCI-H322M_lapatinib_10000nM_24h NCI-H322M lapatinib 10000nM 24h
## 171 NCI-H322M_lapatinib_0nM_24h NCI-H322M lapatinib 0nM 24h
## 172 NCI-H460_lapatinib_10000nM_24h NCI-H460 lapatinib 10000nM 24h
## 173 NCI-H460_lapatinib_0nM_24h NCI-H460 lapatinib 0nM 24h
## 174 NCI-H522_lapatinib_10000nM_24h NCI-H522 lapatinib 10000nM 24h
## 175 NCI-H522_lapatinib_0nM_24h NCI-H522 lapatinib 0nM 24h
## 176 OVCAR-3_lapatinib_10000nM_24h OVCAR-3 lapatinib 10000nM 24h
## 177 OVCAR-3_lapatinib_0nM_24h OVCAR-3 lapatinib 0nM 24h
## 178 OVCAR-4_lapatinib_10000nM_24h OVCAR-4 lapatinib 10000nM 24h
## 179 OVCAR-4_lapatinib_0nM_24h OVCAR-4 lapatinib 0nM 24h
## 180 OVCAR-5_lapatinib_10000nM_24h OVCAR-5 lapatinib 10000nM 24h
## 181 OVCAR-5_lapatinib_0nM_24h OVCAR-5 lapatinib 0nM 24h
## 182 OVCAR-8_lapatinib_10000nM_24h OVCAR-8 lapatinib 10000nM 24h
## 183 OVCAR-8_lapatinib_0nM_24h OVCAR-8 lapatinib 0nM 24h
## 184 PC-3_lapatinib_10000nM_24h PC-3 lapatinib 10000nM 24h
## 185 PC-3_lapatinib_0nM_24h PC-3 lapatinib 0nM 24h
## 186 RPMI-8226_lapatinib_10000nM_24h RPMI-8226 lapatinib 10000nM 24h
## 187 RPMI-8226_lapatinib_0nM_24h RPMI-8226 lapatinib 0nM 24h
## 188 RXF-393_lapatinib_10000nM_24h RXF-393 lapatinib 10000nM 24h
## 189 RXF-393_lapatinib_0nM_24h RXF-393 lapatinib 0nM 24h
## 190 SF-268_lapatinib_10000nM_24h SF-268 lapatinib 10000nM 24h
## 191 SF-268_lapatinib_0nM_24h SF-268 lapatinib 0nM 24h
## 192 SF-295_lapatinib_10000nM_24h SF-295 lapatinib 10000nM 24h
## 193 SF-295_lapatinib_0nM_24h SF-295 lapatinib 0nM 24h
## 194 SF-539_lapatinib_10000nM_24h SF-539 lapatinib 10000nM 24h
## 195 SF-539_lapatinib_0nM_24h SF-539 lapatinib 0nM 24h
## 196 SK-MEL-2_lapatinib_10000nM_24h SK-MEL-2 lapatinib 10000nM 24h
## 197 SK-MEL-2_lapatinib_0nM_24h SK-MEL-2 lapatinib 0nM 24h
## 198 SK-MEL-28_lapatinib_10000nM_24h SK-MEL-28 lapatinib 10000nM 24h
## 199 SK-MEL-28_lapatinib_0nM_24h SK-MEL-28 lapatinib 0nM 24h
## 200 SK-MEL-5_lapatinib_10000nM_24h SK-MEL-5 lapatinib 10000nM 24h
## 201 SK-MEL-5_lapatinib_0nM_24h SK-MEL-5 lapatinib 0nM 24h
## 202 SK-OV-3_lapatinib_10000nM_24h SK-OV-3 lapatinib 10000nM 24h
## 203 SK-OV-3_lapatinib_0nM_24h SK-OV-3 lapatinib 0nM 24h
## 204 SN12C_lapatinib_10000nM_24h SN12C lapatinib 10000nM 24h
## 205 SN12C_lapatinib_0nM_24h SN12C lapatinib 0nM 24h
## 206 SNB-19_lapatinib_10000nM_24h SNB-19 lapatinib 10000nM 24h
## 207 SNB-19_lapatinib_0nM_24h SNB-19 lapatinib 0nM 24h
## 208 SNB-75_lapatinib_10000nM_24h SNB-75 lapatinib 10000nM 24h
## 209 SNB-75_lapatinib_0nM_24h SNB-75 lapatinib 0nM 24h
## 210 <NA> SR <NA> <NA> <NA>
## 211 SW-620_lapatinib_10000nM_24h SW-620 lapatinib 10000nM 24h
## 212 SW-620_lapatinib_0nM_24h SW-620 lapatinib 0nM 24h
## 213 T-47D_lapatinib_10000nM_24h T-47D lapatinib 10000nM 24h
## 214 T-47D_lapatinib_0nM_24h T-47D lapatinib 0nM 24h
## 215 TK-10_lapatinib_10000nM_24h TK-10 lapatinib 10000nM 24h
## 216 TK-10_lapatinib_0nM_24h TK-10 lapatinib 0nM 24h
## 217 U251_lapatinib_10000nM_24h U251 lapatinib 10000nM 24h
## 218 U251_lapatinib_0nM_24h U251 lapatinib 0nM 24h
## 219 UACC-257_lapatinib_10000nM_24h UACC-257 lapatinib 10000nM 24h
## 220 UACC-257_lapatinib_0nM_24h UACC-257 lapatinib 0nM 24h
## 221 UACC-62_lapatinib_10000nM_24h UACC-62 lapatinib 10000nM 24h
## 222 UACC-62_lapatinib_0nM_24h UACC-62 lapatinib 0nM 24h
## 223 UO-31_lapatinib_10000nM_24h UO-31 lapatinib 10000nM 24h
## 224 UO-31_lapatinib_0nM_24h UO-31 lapatinib 0nM 24h
## tissue.x sample.y drug.y dose.y time.y
## 1 Renal 786-0_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 2 Renal 786-0_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 3 Renal A498_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 4 Renal A498_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 5 Lung A549_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 6 Lung A549_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 7 Renal ACHN_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 8 Renal ACHN_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 9 Breast BT-549_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 10 Breast BT-549_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 11 Renal CAKI-1_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 12 Renal CAKI-1_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 13 <NA> CCRF-CEM_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 14 Prostate DU-145_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 15 Prostate DU-145_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 16 Lung EKVX_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 17 Lung EKVX_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 18 Colon HCC-2998_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 19 Colon HCC-2998_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 20 Colon HCT-116_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 21 Colon HCT-116_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 22 Colon HCT-15_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 23 Colon HCT-15_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 24 <NA> HL-60_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 25 Lung HOP-62_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 26 Lung HOP-62_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 27 Lung HOP-92_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 28 Lung HOP-92_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 29 Breast HS-578T_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 30 Breast HS-578T_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 31 <NA> HT29_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 32 Ovarian IGR-OV1_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 33 Ovarian IGR-OV1_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 34 <NA> K-562_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 35 Colon KM12_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 36 Colon KM12_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 37 <NA> LOX_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 38 Melanoma M14_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 39 Melanoma M14_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 40 Melanoma MALME-3M_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 41 Melanoma MALME-3M_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 42 Breast MCF7_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 43 Breast MCF7_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 44 Breast MDA-MB-231_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 45 Breast MDA-MB-231_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 46 Melanoma MDA-MB-435_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 47 Melanoma MDA-MB-435_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 48 Breast MDA-MB-468_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 49 Breast MDA-MB-468_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 50 Leukemia MOLT-4_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 51 Leukemia MOLT-4_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 52 Ovarian NCI-ADR-RES_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 53 Ovarian NCI-ADR-RES_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 54 Lung NCI-H226_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 55 Lung NCI-H226_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 56 Lung NCI-H23_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 57 Lung NCI-H23_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 58 Lung NCI-H322M_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 59 Lung NCI-H322M_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 60 Lung NCI-H460_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 61 Lung NCI-H460_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 62 Lung NCI-H522_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 63 Lung NCI-H522_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 64 Ovarian OVCAR-3_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 65 Ovarian OVCAR-3_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 66 Ovarian OVCAR-4_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 67 Ovarian OVCAR-4_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 68 Ovarian OVCAR-5_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 69 Ovarian OVCAR-5_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 70 Ovarian OVCAR-8_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 71 Ovarian OVCAR-8_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 72 Prostate PC-3_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 73 Prostate PC-3_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 74 Leukemia RPMI-8226_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 75 Leukemia RPMI-8226_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 76 Renal RXF-393_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 77 Renal RXF-393_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 78 CNS SF-268_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 79 CNS SF-268_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 80 CNS SF-295_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 81 CNS SF-295_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 82 CNS SF-539_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 83 CNS SF-539_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 84 Melanoma SK-MEL-2_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 85 Melanoma SK-MEL-2_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 86 Melanoma SK-MEL-28_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 87 Melanoma SK-MEL-28_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 88 Melanoma SK-MEL-5_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 89 Melanoma SK-MEL-5_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 90 Ovarian SK-OV-3_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 91 Ovarian SK-OV-3_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 92 Renal SN12C_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 93 Renal SN12C_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 94 CNS SNB-19_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 95 CNS SNB-19_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 96 CNS SNB-75_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 97 CNS SNB-75_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 98 <NA> SR_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 99 Colon SW-620_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 100 Colon SW-620_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 101 Breast T-47D_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 102 Breast T-47D_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 103 Renal TK-10_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 104 Renal TK-10_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 105 CNS U251_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 106 CNS U251_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 107 Melanoma UACC-257_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 108 Melanoma UACC-257_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 109 Melanoma UACC-62_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 110 Melanoma UACC-62_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 111 Renal UO-31_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 112 Renal UO-31_erlotinib_10000nM_24h erlotinib 10000nM 24h
## 113 Renal 786-0_erlotinib_0nM_24h erlotinib 0nM 24h
## 114 Renal 786-0_erlotinib_0nM_24h erlotinib 0nM 24h
## 115 Renal A498_erlotinib_0nM_24h erlotinib 0nM 24h
## 116 Renal A498_erlotinib_0nM_24h erlotinib 0nM 24h
## 117 Lung A549_erlotinib_0nM_24h erlotinib 0nM 24h
## 118 Lung A549_erlotinib_0nM_24h erlotinib 0nM 24h
## 119 Renal ACHN_erlotinib_0nM_24h erlotinib 0nM 24h
## 120 Renal ACHN_erlotinib_0nM_24h erlotinib 0nM 24h
## 121 Breast BT-549_erlotinib_0nM_24h erlotinib 0nM 24h
## 122 Breast BT-549_erlotinib_0nM_24h erlotinib 0nM 24h
## 123 Renal CAKI-1_erlotinib_0nM_24h erlotinib 0nM 24h
## 124 Renal CAKI-1_erlotinib_0nM_24h erlotinib 0nM 24h
## 125 <NA> CCRF-CEM_erlotinib_0nM_24h erlotinib 0nM 24h
## 126 Prostate DU-145_erlotinib_0nM_24h erlotinib 0nM 24h
## 127 Prostate DU-145_erlotinib_0nM_24h erlotinib 0nM 24h
## 128 Lung EKVX_erlotinib_0nM_24h erlotinib 0nM 24h
## 129 Lung EKVX_erlotinib_0nM_24h erlotinib 0nM 24h
## 130 Colon HCC-2998_erlotinib_0nM_24h erlotinib 0nM 24h
## 131 Colon HCC-2998_erlotinib_0nM_24h erlotinib 0nM 24h
## 132 Colon HCT-116_erlotinib_0nM_24h erlotinib 0nM 24h
## 133 Colon HCT-116_erlotinib_0nM_24h erlotinib 0nM 24h
## 134 Colon HCT-15_erlotinib_0nM_24h erlotinib 0nM 24h
## 135 Colon HCT-15_erlotinib_0nM_24h erlotinib 0nM 24h
## 136 <NA> HL-60_erlotinib_0nM_24h erlotinib 0nM 24h
## 137 Lung HOP-62_erlotinib_0nM_24h erlotinib 0nM 24h
## 138 Lung HOP-62_erlotinib_0nM_24h erlotinib 0nM 24h
## 139 Lung HOP-92_erlotinib_0nM_24h erlotinib 0nM 24h
## 140 Lung HOP-92_erlotinib_0nM_24h erlotinib 0nM 24h
## 141 Breast HS-578T_erlotinib_0nM_24h erlotinib 0nM 24h
## 142 Breast HS-578T_erlotinib_0nM_24h erlotinib 0nM 24h
## 143 <NA> HT29_erlotinib_0nM_24h erlotinib 0nM 24h
## 144 Ovarian IGR-OV1_erlotinib_0nM_24h erlotinib 0nM 24h
## 145 Ovarian IGR-OV1_erlotinib_0nM_24h erlotinib 0nM 24h
## 146 <NA> K-562_erlotinib_0nM_24h erlotinib 0nM 24h
## 147 Colon KM12_erlotinib_0nM_24h erlotinib 0nM 24h
## 148 Colon KM12_erlotinib_0nM_24h erlotinib 0nM 24h
## 149 <NA> LOX_erlotinib_0nM_24h erlotinib 0nM 24h
## 150 Melanoma M14_erlotinib_0nM_24h erlotinib 0nM 24h
## 151 Melanoma M14_erlotinib_0nM_24h erlotinib 0nM 24h
## 152 Melanoma MALME-3M_erlotinib_0nM_24h erlotinib 0nM 24h
## 153 Melanoma MALME-3M_erlotinib_0nM_24h erlotinib 0nM 24h
## 154 Breast MCF7_erlotinib_0nM_24h erlotinib 0nM 24h
## 155 Breast MCF7_erlotinib_0nM_24h erlotinib 0nM 24h
## 156 Breast MDA-MB-231_erlotinib_0nM_24h erlotinib 0nM 24h
## 157 Breast MDA-MB-231_erlotinib_0nM_24h erlotinib 0nM 24h
## 158 Melanoma MDA-MB-435_erlotinib_0nM_24h erlotinib 0nM 24h
## 159 Melanoma MDA-MB-435_erlotinib_0nM_24h erlotinib 0nM 24h
## 160 Breast MDA-MB-468_erlotinib_0nM_24h erlotinib 0nM 24h
## 161 Breast MDA-MB-468_erlotinib_0nM_24h erlotinib 0nM 24h
## 162 Leukemia MOLT-4_erlotinib_0nM_24h erlotinib 0nM 24h
## 163 Leukemia MOLT-4_erlotinib_0nM_24h erlotinib 0nM 24h
## 164 Ovarian NCI-ADR-RES_erlotinib_0nM_24h erlotinib 0nM 24h
## 165 Ovarian NCI-ADR-RES_erlotinib_0nM_24h erlotinib 0nM 24h
## 166 Lung NCI-H226_erlotinib_0nM_24h erlotinib 0nM 24h
## 167 Lung NCI-H226_erlotinib_0nM_24h erlotinib 0nM 24h
## 168 Lung NCI-H23_erlotinib_0nM_24h erlotinib 0nM 24h
## 169 Lung NCI-H23_erlotinib_0nM_24h erlotinib 0nM 24h
## 170 Lung NCI-H322M_erlotinib_0nM_24h erlotinib 0nM 24h
## 171 Lung NCI-H322M_erlotinib_0nM_24h erlotinib 0nM 24h
## 172 Lung NCI-H460_erlotinib_0nM_24h erlotinib 0nM 24h
## 173 Lung NCI-H460_erlotinib_0nM_24h erlotinib 0nM 24h
## 174 Lung NCI-H522_erlotinib_0nM_24h erlotinib 0nM 24h
## 175 Lung NCI-H522_erlotinib_0nM_24h erlotinib 0nM 24h
## 176 Ovarian OVCAR-3_erlotinib_0nM_24h erlotinib 0nM 24h
## 177 Ovarian OVCAR-3_erlotinib_0nM_24h erlotinib 0nM 24h
## 178 Ovarian OVCAR-4_erlotinib_0nM_24h erlotinib 0nM 24h
## 179 Ovarian OVCAR-4_erlotinib_0nM_24h erlotinib 0nM 24h
## 180 Ovarian OVCAR-5_erlotinib_0nM_24h erlotinib 0nM 24h
## 181 Ovarian OVCAR-5_erlotinib_0nM_24h erlotinib 0nM 24h
## 182 Ovarian OVCAR-8_erlotinib_0nM_24h erlotinib 0nM 24h
## 183 Ovarian OVCAR-8_erlotinib_0nM_24h erlotinib 0nM 24h
## 184 Prostate PC-3_erlotinib_0nM_24h erlotinib 0nM 24h
## 185 Prostate PC-3_erlotinib_0nM_24h erlotinib 0nM 24h
## 186 Leukemia RPMI-8226_erlotinib_0nM_24h erlotinib 0nM 24h
## 187 Leukemia RPMI-8226_erlotinib_0nM_24h erlotinib 0nM 24h
## 188 Renal RXF-393_erlotinib_0nM_24h erlotinib 0nM 24h
## 189 Renal RXF-393_erlotinib_0nM_24h erlotinib 0nM 24h
## 190 CNS SF-268_erlotinib_0nM_24h erlotinib 0nM 24h
## 191 CNS SF-268_erlotinib_0nM_24h erlotinib 0nM 24h
## 192 CNS SF-295_erlotinib_0nM_24h erlotinib 0nM 24h
## 193 CNS SF-295_erlotinib_0nM_24h erlotinib 0nM 24h
## 194 CNS SF-539_erlotinib_0nM_24h erlotinib 0nM 24h
## 195 CNS SF-539_erlotinib_0nM_24h erlotinib 0nM 24h
## 196 Melanoma SK-MEL-2_erlotinib_0nM_24h erlotinib 0nM 24h
## 197 Melanoma SK-MEL-2_erlotinib_0nM_24h erlotinib 0nM 24h
## 198 Melanoma SK-MEL-28_erlotinib_0nM_24h erlotinib 0nM 24h
## 199 Melanoma SK-MEL-28_erlotinib_0nM_24h erlotinib 0nM 24h
## 200 Melanoma SK-MEL-5_erlotinib_0nM_24h erlotinib 0nM 24h
## 201 Melanoma SK-MEL-5_erlotinib_0nM_24h erlotinib 0nM 24h
## 202 Ovarian SK-OV-3_erlotinib_0nM_24h erlotinib 0nM 24h
## 203 Ovarian SK-OV-3_erlotinib_0nM_24h erlotinib 0nM 24h
## 204 Renal SN12C_erlotinib_0nM_24h erlotinib 0nM 24h
## 205 Renal SN12C_erlotinib_0nM_24h erlotinib 0nM 24h
## 206 CNS SNB-19_erlotinib_0nM_24h erlotinib 0nM 24h
## 207 CNS SNB-19_erlotinib_0nM_24h erlotinib 0nM 24h
## 208 CNS SNB-75_erlotinib_0nM_24h erlotinib 0nM 24h
## 209 CNS SNB-75_erlotinib_0nM_24h erlotinib 0nM 24h
## 210 <NA> SR_erlotinib_0nM_24h erlotinib 0nM 24h
## 211 Colon SW-620_erlotinib_0nM_24h erlotinib 0nM 24h
## 212 Colon SW-620_erlotinib_0nM_24h erlotinib 0nM 24h
## 213 Breast T-47D_erlotinib_0nM_24h erlotinib 0nM 24h
## 214 Breast T-47D_erlotinib_0nM_24h erlotinib 0nM 24h
## 215 Renal TK-10_erlotinib_0nM_24h erlotinib 0nM 24h
## 216 Renal TK-10_erlotinib_0nM_24h erlotinib 0nM 24h
## 217 CNS U251_erlotinib_0nM_24h erlotinib 0nM 24h
## 218 CNS U251_erlotinib_0nM_24h erlotinib 0nM 24h
## 219 Melanoma UACC-257_erlotinib_0nM_24h erlotinib 0nM 24h
## 220 Melanoma UACC-257_erlotinib_0nM_24h erlotinib 0nM 24h
## 221 Melanoma UACC-62_erlotinib_0nM_24h erlotinib 0nM 24h
## 222 Melanoma UACC-62_erlotinib_0nM_24h erlotinib 0nM 24h
## 223 Renal UO-31_erlotinib_0nM_24h erlotinib 0nM 24h
## 224 Renal UO-31_erlotinib_0nM_24h erlotinib 0nM 24h
## tissue.y
## 1 Renal
## 2 Renal
## 3 Renal
## 4 Renal
## 5 Lung
## 6 Lung
## 7 Renal
## 8 Renal
## 9 Breast
## 10 Breast
## 11 Renal
## 12 Renal
## 13 Leukemia
## 14 Prostate
## 15 Prostate
## 16 Lung
## 17 Lung
## 18 Colon
## 19 Colon
## 20 Colon
## 21 Colon
## 22 Colon
## 23 Colon
## 24 Leukemia
## 25 Lung
## 26 Lung
## 27 Lung
## 28 Lung
## 29 Breast
## 30 Breast
## 31 Colon
## 32 Ovarian
## 33 Ovarian
## 34 Leukemia
## 35 Colon
## 36 Colon
## 37 Melanoma
## 38 Melanoma
## 39 Melanoma
## 40 Melanoma
## 41 Melanoma
## 42 Breast
## 43 Breast
## 44 Breast
## 45 Breast
## 46 Melanoma
## 47 Melanoma
## 48 Breast
## 49 Breast
## 50 Leukemia
## 51 Leukemia
## 52 Ovarian
## 53 Ovarian
## 54 Lung
## 55 Lung
## 56 Lung
## 57 Lung
## 58 Lung
## 59 Lung
## 60 Lung
## 61 Lung
## 62 Lung
## 63 Lung
## 64 Ovarian
## 65 Ovarian
## 66 Ovarian
## 67 Ovarian
## 68 Ovarian
## 69 Ovarian
## 70 Ovarian
## 71 Ovarian
## 72 Prostate
## 73 Prostate
## 74 Leukemia
## 75 Leukemia
## 76 Renal
## 77 Renal
## 78 CNS
## 79 CNS
## 80 CNS
## 81 CNS
## 82 CNS
## 83 CNS
## 84 Melanoma
## 85 Melanoma
## 86 Melanoma
## 87 Melanoma
## 88 Melanoma
## 89 Melanoma
## 90 Ovarian
## 91 Ovarian
## 92 Renal
## 93 Renal
## 94 CNS
## 95 CNS
## 96 CNS
## 97 CNS
## 98 Leukemia
## 99 Colon
## 100 Colon
## 101 Breast
## 102 Breast
## 103 Renal
## 104 Renal
## 105 CNS
## 106 CNS
## 107 Melanoma
## 108 Melanoma
## 109 Melanoma
## 110 Melanoma
## 111 Renal
## 112 Renal
## 113 Renal
## 114 Renal
## 115 Renal
## 116 Renal
## 117 Lung
## 118 Lung
## 119 Renal
## 120 Renal
## 121 Breast
## 122 Breast
## 123 Renal
## 124 Renal
## 125 Leukemia
## 126 Prostate
## 127 Prostate
## 128 Lung
## 129 Lung
## 130 Colon
## 131 Colon
## 132 Colon
## 133 Colon
## 134 Colon
## 135 Colon
## 136 Leukemia
## 137 Lung
## 138 Lung
## 139 Lung
## 140 Lung
## 141 Breast
## 142 Breast
## 143 Colon
## 144 Ovarian
## 145 Ovarian
## 146 Leukemia
## 147 Colon
## 148 Colon
## 149 Melanoma
## 150 Melanoma
## 151 Melanoma
## 152 Melanoma
## 153 Melanoma
## 154 Breast
## 155 Breast
## 156 Breast
## 157 Breast
## 158 Melanoma
## 159 Melanoma
## 160 Breast
## 161 Breast
## 162 Leukemia
## 163 Leukemia
## 164 Ovarian
## 165 Ovarian
## 166 Lung
## 167 Lung
## 168 Lung
## 169 Lung
## 170 Lung
## 171 Lung
## 172 Lung
## 173 Lung
## 174 Lung
## 175 Lung
## 176 Ovarian
## 177 Ovarian
## 178 Ovarian
## 179 Ovarian
## 180 Ovarian
## 181 Ovarian
## 182 Ovarian
## 183 Ovarian
## 184 Prostate
## 185 Prostate
## 186 Leukemia
## 187 Leukemia
## 188 Renal
## 189 Renal
## 190 CNS
## 191 CNS
## 192 CNS
## 193 CNS
## 194 CNS
## 195 CNS
## 196 Melanoma
## 197 Melanoma
## 198 Melanoma
## 199 Melanoma
## 200 Melanoma
## 201 Melanoma
## 202 Ovarian
## 203 Ovarian
## 204 Renal
## 205 Renal
## 206 CNS
## 207 CNS
## 208 CNS
## 209 CNS
## 210 Leukemia
## 211 Colon
## 212 Colon
## 213 Breast
## 214 Breast
## 215 Renal
## 216 Renal
## 217 CNS
## 218 CNS
## 219 Melanoma
## 220 Melanoma
## 221 Melanoma
## 222 Melanoma
## 223 Renal
## 224 Renalrmv.rows = apply(el, 1, function(x) {
sum(is.na(x))
}) # Go through each row and sum up all missing values
row.names(rmv.rows)Create data frame with lapatinib and erlotinib data
fc<-(Treated-Untreated)
fc<-data.frame(scale(fc))
all<-data.frame(fc[grep("lapatinib|erlotinib", colnames(fc))])since erlotinip contains more columns than lapatinib, we have to remove these columns
all.rmv<-all[, -which(colnames(all) %in% c(
"CCRF.CEM_erlotinib_0nM_24h",
"HL.60_erlotinib_0nM_24h",
"HT29_erlotinib_0nM_24h",
"K.562_erlotinib_0nM_24h",
"LOX_erlotinib_0nM_24h",
"SR_erlotinib_0nM_24h",
"COLO.205_lapatinib_0nM_24h"))]Checking the rows
la<-data.frame(all.rmv[grep("lapatinib", colnames(all.rmv))])
ncol(la)## [1] 0er<-data.frame(all.rmv[grep("erlotinib", colnames(all.rmv))])
ncol(er)## [1] 0erla<-data.frame(er,la)
ncol(all.rmv) #to prove if the columns are removed## [1] 0Anova
p = 0.2 means that the result does not differ significantly. Thus, the two drugs did not differ significantly from each other.
